tribe29 GmbH, supplier of the monitoring solution Checkmk, announced the availability of Checkmk version 1.6, offering enhanced monitoring capabilities for cloud and container environments as well as a new daemon for dynamic host configuration. The new Checkmk version includes new and improved features especially for monitoring cloud environments and container infrastructures. For example, Checkmk retrieves data from Amazon Web Services directly via the AWS HTTP API and monitors all major AWS services. To keep the cost for users low, the Checkmk developers have written the agent in such a way that requires as few costly API calls as possible. On top of that, they've implemented checks for monitoring AWS costs too, so that users always can get alerted about exploding costs. Azure users also find some new plug-ins that can monitor storage space, databases and virtual machines and cost of the Microsoft cloud. Checkmk uses the Azure API to communicate with the cloud. All Azure resources are automatically integrated into Checkmk, thus heavily reducing the required configuration effort. Checkmk 1.6 also contains improved checks for Docker, Kubernetes and OpenShift. The monitoring solution keeps an eye on clusters, nodes, and persistent storage as well as on pods, deployments, and micro services. Docker monitoring in particular has changed: The developers have completely revised the Docker check, enhancing and simplifying it and ensuring that it works even for older Docker versions. Checkmk 1.6 introduces the concept of labels for hosts and services. A host can have an unlimited number of labels. They work similarly to tags and can be used to create conditions for Checkmk rules. Labels are widely used in container and cloud environments, and Checkmk automatically detects and adopt them for better visibility of services and powerful configuration options. In cloud and container environments the number of hosts changes frequently because new ones are being created automatically, and old ones vanish. Since it's impractical to update the Checkmk configuration manually, the new version of the Checkmk Enterprise Edition (CEE) provides a brand-new dynamic configuration daemon (DCD). It simplifies the configuration process significantly by automatically detecting Kubernetes nodes, AWS EC2 instances, Azure resource groups, vSphere hosts and much more – thee daemon even removes hosts that no longer exist from the Checkmk monitoring. The developers have added plenty of new checks, i.e. for monitoring Elasticsearch, Splunk, SAP Hana, Oracle, Cisco UCS, Enviromux, Checkpoint, Dell, Fujitsu, and HP Management Boards. More than 100 new plug-ins have arrived in Checkmk 1.6 since version 1.5. The total number of Checkmk plug-ins has thus increased to 1700. In addition, the monitoring solution now works with i-doit, Slack, ServiceNow, JIRA, Opsgenie, VictorOps, PagerDuty and Mattermost. The new integrations ensure that Checkmk can "fill" the external platforms on its own. For example, notification rules can automatically create tickets for JIRA or ServiceNow.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...